Contributors:

William Loving (wfl9zy) James Sweat (jes9hd)

Goals:

  • Our goal here in part 3 is to explore jobs and job satisfaction on a more granular level.
  • Find out whether or not older individuals favor non-travel working styles or overtime positions.
  • Find out if job role and distance from home have an impact on general job satisfaction.
  • Find out if job satisfaction declines as the number of years since promotion rises.
  • Find out if the marital status of individuals when working has a general trend, are more people married? Divorced? Single?

Part 3: Job Satisfaction Outside of CS/DS:

  • This data more-so concerns people and how they felt about their jobs rather than data about the job itself.
library(readr)
library(dplyr)
library(ggplot2)
library(plotly)

Load Data:

data <- read_csv("../data/job-satisfaction-data/hr_data.csv.csv")
head(data)
## # A tibble: 6 × 35
##     Age Attrition BusinessTravel DailyRate Department DistanceFromHome Education
##   <dbl> <chr>     <chr>              <dbl> <chr>                 <dbl>     <dbl>
## 1    41 Yes       Travel_Rarely       1102 Sales                     1         2
## 2    49 No        Travel_Freque…       279 Research …                8         1
## 3    37 Yes       Travel_Rarely       1373 Research …                2         2
## 4    33 No        Travel_Freque…      1392 Research …                3         4
## 5    27 No        Travel_Rarely        591 Research …                2         1
## 6    32 No        Travel_Freque…      1005 Research …                2         2
## # ℹ 28 more variables: EducationField <chr>, EmployeeCount <dbl>,
## #   EmployeeNumber <dbl>, EnvironmentSatisfaction <dbl>, Gender <chr>,
## #   HourlyRate <dbl>, JobInvolvement <dbl>, JobLevel <dbl>, JobRole <chr>,
## #   JobSatisfaction <dbl>, MaritalStatus <chr>, MonthlyIncome <dbl>,
## #   MonthlyRate <dbl>, NumCompaniesWorked <dbl>, Over18 <chr>, OverTime <chr>,
## #   PercentSalaryHike <dbl>, PerformanceRating <dbl>,
## #   RelationshipSatisfaction <dbl>, StandardHours <dbl>, …

Make Some Transformations for Ease of Understanding:

data$JobSatisfaction <- as.factor(data$JobSatisfaction)

Create Visuals:

Plot 1:

  • This visual allows us to discern some interesting things. For example, Managers that travel frequently tend to be paid less, the reverse is true for Manufacturing Directors that don’t travel.

Plot 2:

  • Here we can gleam some small correlations, perhaps young people simply aren’t made for management, manufacturing, or researching positions.

Plot 3:

  • Here we can discern that as the years since promotion go on, the job satisfaction definitely wavers, especially if the monthly rate is low and the years since promotion is high.

Plot 4:

  • Here we are simply showing that most working class people tend to be married regardless of their gender, we have significantly more monthly rate data-points for married individuals than Divorced or Single.

Plot 5: